Method and device for adjusting parameters of lidar, and lidar

ABSTRACT

A method and a device for adjusting parameters of LiDAR and a LiDAR are provided. The method includes: acquiring 3D environment information around the LiDAR; identifying a scenario type where the LiDAR is positioned and a drivable area based on the 3D environment information; determining a parameter adjusting strategy of the LiDAR based on the scenario type and the drivable area; and adjusting current operating parameters of the LiDAR based on the parameter adjusting strategy.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 18/094,346, filed on Jan. 7, 2023, which is a continuation ofInternational Application No. PCT/CN2021/105048, filed on Jul. 7, 2021,which claims the benefit of priority to China Patent Application No. CN202010650848.4, filed on Jul. 8, 2020, the contents of which areincorporated herein by reference in their entireties.

TECHNICAL FIELD

Embodiments of the present disclosure relate to the field of radartechnology, and more particularly, to a method and a device foradjusting parameters of LiDAR, and a LiDAR.

BACKGROUND

Currently, because of its excellent characteristics and strongadaptability to an external environment, a LiDAR has been widely used inautomatic driving, aided driving and other fields.

The LiDAR in a prior art generally maintains a fixed working frequency,a fixed detection angle and a fixed range, and does not have ability todynamically adjust operating parameters, so that no matter what type ofscenario is provided, the LiDAR maintains the same parameters.

SUMMARY

One objective of embodiments of the present disclosure is to provide amethod and device for adjusting parameters of LiDAR, and a LiDAR, whichcan automatically adjust operating parameters of the LiDAR according todifferent scenarios.

According to one aspect of an embodiment of the present disclosure,there is provided a method for adjusting parameters of a LiDAR,including the following steps: acquiring 3D environment informationaround the LiDAR; identifying a scenario type where the LiDAR ispositioned and a drivable area based on the 3D environment information;determining a parameter adjusting strategy of the LiDAR based on thescenario type and the drivable area; and adjusting current operatingparameters of the LiDAR based on the parameter adjusting strategy.

In some embodiments, the 3D environment information includes 3D pointcloud data. The step of identifying the scenario type where the LiDAR ispositioned and the drivable area based on the 3D environment informationspecifically includes: processing the 3D point cloud data to generate amulti-channel point cloud feature map; extracting high-dimensionalfeature information from the multi-channel point cloud feature map;determining the scenario type according to the high-dimensional featureinformation; and determining the drivable area according to thehigh-dimensional feature information.

In some embodiments, the step of extracting the high-dimensional featureinformation from the multi-channel point cloud feature map specificallyincludes: inputting the multi-channel point cloud feature map into afirst neural network, and acquiring the high-dimensional featureinformation output by the first neural network.

The step of determining the scenario type according to thehigh-dimensional feature information specifically includes: inputtingthe high-dimensional feature information into a second neural network;acquiring an output value of the scenario type output by the secondneural network; and determining the scenario type corresponding to theoutput value of the scenario type according to a correspondingrelationship between an output value of a preset scenario type and ascenario type label.

The step of determining the drivable area according to thehigh-dimensional feature information specifically includes: inputtingthe high-dimensional feature information into a third neural network;acquiring an output map of the drivable area output by the third neuralnetwork; and determining the drivable area corresponding to the outputmap of the drivable area according to a corresponding relationshipbetween an output map of a preset drivable area and a drivable arealabel.

In some embodiments, the steps of determining the parameter adjustingstrategy of the LiDAR based on the scenario type and the drivable area,and adjusting the current operating parameters of the LiDAR based on theparameter adjusting strategy specifically include: according to thescenario type, determining scenario parameter adjusting strategies ofone or more of a horizontal angle of field of view of the LiDAR, avertical angle of field of view of the LiDAR, a direction of an opticalaxis of an emitting laser beam of the LiDAR, a scanning density of theLiDAR, a scanning frequency of the LiDAR, and a pulse emitting power ofthe LiDAR; adjusting the scenario parameter adjusting strategy accordingto the drivable area; determining the adjusted scenario parameteradjusting strategy as the parameter adjusting strategy of the LiDAR, andadjusting the current operating parameters of the LiDAR based on theparameter adjusting strategy.

In some embodiments, the steps of adjusting the scenario parameteradjusting strategy according to the drivable area, determining theadjusted scenario parameter adjusting strategy as the parameteradjusting strategy of the LiDAR, and adjusting the current operatingparameters of the LiDAR based on the parameter adjusting strategyspecifically include: according to the scenario type, acquiring astandard driving area in the scenario type and driving parameteradjusting strategies of the horizontal angle of field of view of theLiDAR and the vertical angle of field of view of the LiDAR thatcorrespond to the standard driving area, matching the standard drivingarea with the drivable area, adjusting the scenario parameter adjustingstrategy according to the matching results and the driving parameteradjusting strategy, and adjusting the current operating parameters ofthe LiDAR based on the adjusted scenario parameter adjusting strategy.

In some embodiments, the steps of matching the standard driving areawith the drivable area, and adjusting the scenario parameter adjustingstrategy according to the matching results and the driving parameteradjusting strategy specifically include: analyzing a topologicalstructure of the drivable area and a topological structure of thestandard driving area; calculating a topological correlation between thetopological structure of the drivable area and the topological structureof the standard driving area; determining a scenario differencecoefficient of the drivable area relative to the standard driving areaaccording to the topological correlation; and adjusting the scenarioparameter adjusting strategy according to the scenario differencecoefficient and the driving parameter adjusting strategy.

In some embodiments, the current operating parameters of the LiDARcomprise one or more of a vertical angle of field of view of an emittinglaser beam of the LiDAR, a horizontal angle of field of view of theemitting laser beam of the LiDAR, a direction of an optical axis of theemitting laser beam of the LiDAR, a scanning frequency of the LiDAR, anda pulse emitting frequency of the LiDAR.

According to another aspect of an embodiment of the present disclosure,there is provided a device for adjusting parameters of a LiDAR,including: an environment information acquisition module, configured toacquire 3D environment information around the LiDAR; an identificationmodule, configured to identify a scenario type where the LiDAR ispositioned and a drivable area based on the 3D environment information;and an adjusting module, configured to determine a parameter adjustingstrategy of the LiDAR based on the scenario type and the drivable areaand adjust current operating parameters of the LiDAR based on theparameter adjusting strategy.

According to yet another aspect of the embodiment of the presentdisclosure, there is provided a LiDAR, including an emitting device, areceiving device, a processor, a memory, a communication interface, anda communication bus, where the processor, the memory, and thecommunication interface complete mutual communication via thecommunication bus. The emitting device is configured to emit emergentlaser to a detection area. The receiving device is configured to receiveecho laser reflected by an object in the detection area. The memory isconfigured to store at least one executable instruction, and theexecutable instruction causes the processor to execute the steps of themethod for adjusting the parameters of the LiDAR described above.

According to another aspect of an embodiment of the present disclosure,there is provided a method for adjusting parameters of LiDAR, which isapplied to a LiDAR system including the plurality of LiDARs, and themethod includes: acquiring 3D environmental information around the LiDARsystem; identifying a scenario type where the LiDAR system is positionedand a drivable area based on the 3D environment information; accordingto the scenario type, determining scenario parameter adjustingstrategies of one or more of a horizontal angle of field of view of atleast one LiDAR, a vertical angle of field of view of the LiDAR, adirection of an optical axis of an emitting laser beam of the LiDAR, ascanning density of the LiDAR, a scanning frequency of the LiDAR, and apulse emitting power of the LiDAR of the LiDAR system; and according tothe drivable area, adjusting the scenario parameter adjusting strategyof at least one LiDAR, determining the adjusted scenario parameteradjusting strategy as the parameter adjusting strategy of at least oneLiDAR, and adjusting the current operating parameter of at least oneLiDAR based on the parameter adjusting strategy.

In some embodiments, the step of acquiring the 3D environmentalinformation around the LiDAR system includes:

-   -   acquiring 3D environmental information around the LiDAR system        via one LiDAR in the LiDAR system, where one LiDAR is a LiDAR        that detects a mid-to-far detection field of view; or        alternatively, acquiring the 3D environmental information around        the LiDAR system via the plurality of LiDARs in the LiDAR        system, where the plurality of LiDARs correspond to different        detection fields of view, and the plurality of LiDARs are        integrated to acquire the 3D environment information        respectively to acquire a complete 3D environment information of        the LiDAR system.

In some embodiments, after the step of identifying the scenario typewhere the LiDAR system is positioned and the drivable area based on the3D environment information, the method further includes:

-   -   determining a scanning area range corresponding to the scenario        type according to the scenario type; determining a scanning area        range of each LiDAR in the LiDAR system; and determining whether        the LiDAR in the LiDAR system is in a working state according to        the scanning area range corresponding to the scenario type and        the scanning area range of each LiDAR in the LiDAR system.

In some embodiments, the step of determining whether the LiDAR in theLiDAR system is in the working state according to the scanning arearange corresponding to the scenario type and the scanning area range ofeach LiDAR in the LiDAR system includes: selecting the LiDAR fordetecting the mid-to-far detection field of view in the LiDAR system towork when the identified scenario type is a highway scenario; andselecting all LiDARs in the LiDAR system to work when the identifiedscenario type is an intersection scenario.

In some embodiments, the step of, according to the scenario type,determining scenario parameter adjusting strategies of one or more of ahorizontal angle of field of view of at least one LiDAR, a verticalangle of field of view of the LiDAR, a direction of an optical axis ofan emitting laser beam of the LiDAR, a scanning density of the LiDAR, ascanning frequency of the LiDAR, and a pulse emitting power of the LiDARof the LiDAR system includes:

-   -   acquiring an identifier of each LiDAR in the LiDAR system; and        according to a corresponding relationship among the preset        scenario type, the identifier of at least one LiDAR in the LiDAR        system, and the scenario parameter adjusting strategy,        determining the scenario parameter adjusting strategies of one        or more of the horizontal angle of field of view of at least one        LiDAR, the vertical angle of field of view of the LiDAR, the        direction of the optical axis of the emitting laser beam of the        LiDAR, the scanning density of the LiDAR, and the pulse emitting        power of the LiDAR of the LiDAR system corresponding to the        scenario type and the identifier of at least one LiDAR in the        LiDAR system.

In some embodiments, the steps of adjusting the scenario parameteradjusting strategy of at least one LiDAR according to the drivable area,determining the adjusted scenario parameter adjusting strategy as theparameter adjusting strategy of at least one LiDAR, and adjusting thecurrent operating parameter of at least one LiDAR based on the parameteradjusting strategy specifically include:

-   -   according to the scenario type, acquiring a standard driving        area in the scenario type and driving parameter adjusting        strategies of the horizontal angle of field of view and the        vertical angle of field of view of at least one LiDAR of the        LiDAR system corresponding to the standard driving area;        matching the standard driving area with the drivable area, and        adjusting the scenario parameter adjusting strategy of at least        one LiDAR in the LiDAR system according to matching results and        the driving parameter adjusting strategy of at least one LiDAR        in the LiDAR system; and adjusting the current working parameter        of at least one LiDAR in the LiDAR system based on the scenario        parameter adjusting strategy of at least one LiDAR in the LiDAR        system.

In some embodiments, the steps of matching the standard driving areawith the drivable area, and adjusting the scenario parameter adjustingstrategy of at least one LiDAR in the LiDAR system according to matchingresults and the driving parameter adjusting strategy of at least oneLiDAR in the LiDAR system includes:

-   -   analyzing a topological structure of the drivable area and a        topological structure of the standard driving area; calculating        a topological correlation between the topological structure of        the drivable area and the topological structure of the standard        driving area; determining a scenario difference coefficient of        the drivable area relative to the standard driving area        according to the topological correlation; and adjusting the        scenario parameter adjusting strategy of at least one LiDAR in        the LiDAR system according to the scenario difference        coefficient and the driving parameter adjusting strategy of at        least one LiDAR in the LiDAR system.

In some embodiments, the step of adjusting the scenario parameteradjusting strategy of at least one LiDAR in the LiDAR system accordingto the scenario difference coefficient and the driving parameteradjusting strategy of at least one LiDAR in the LiDAR system includes:acquiring a standard parameter of field of view in the driving parameteradjusting strategy of at least one LiDAR in the LiDAR system; accordingto the scenario difference coefficient, calculating a new parameter offield of view in an equal proportion based on the standard parameter offield of view; and according to the new parameter field of view,adjusting the vertical angle of field of view and the horizontal angleof field of view in the scenario parameter adjusting strategy of atleast one of the LiDAR in the LiDAR system.

According to another aspect of an embodiment of the present disclosure,there is provided a LiDAR system, including a plurality of LiDARs. Theplurality of LiDARs are arranged on the same plane of a carrying deviceof the LiDAR system or arranged on different planes of the carryingdevice of the LiDAR system. The LiDAR comprises an emitting device, areceiving device, a processor, a memory, a communication interface, anda communication bus. The processor, the memory, and the communicationinterface complete mutual communication via the communication bus.

The emitting device is configured to emit the emergent laser to adetection area.

The receiving device is configured to receive echo laser reflected by anobject in the detection area.

The memory is configured to store at least one executable instruction,and the executable instruction causes the processor to execute the stepsof the forgoing method for adjusting the parameters of the LiDAR, andadjust operating parameters of the emitting device.

According to another aspect of an embodiment of the present disclosure,there is provided a computer storage medium. At least one executableinstruction is stored in the computer storage medium, and causes theprocessor to execute the steps of the forgoing method for adjusting theparameters of the LiDAR.

The embodiment of the present disclosure acquires the 3D environmentinformation around the LiDAR, identifies the scenario type where theLiDAR is positioned and the drivable area based on the 3D environmentinformation, determines the parameter adjusting strategy of the LiDARaccording to the scenario type and the drivable area, adjusts thecurrent operating parameters of the LiDAR based on the parameteradjusting strategy. The embodiment of the present disclosure canautomatically adjust the operating parameters of the LiDAR according todifferent scenarios, so that the working state of the LiDAR isautomatically adjusted to a working state suitable for the currentscenario, thereby improving working efficiency of the LiDAR. Further,the embodiment of the present disclosure can also identify the currentscenario and detect the drivable area in real time, so as to adjust thescanning range of the LiDAR to be adapted to the drivable area, whichnot only can improve a detection rate of an obstacle, but also canimprove a utilization rate of computing resources and reduce powerconsumption.

The foregoing descriptions are only brief descriptions of the technicalsolutions in the embodiments of the present disclosure. To understandthe technical means in the embodiments of the present disclosure moreclearly so that the technical means can be carried out according to thecontent of the specification, and to make the foregoing and otherobjectives, characteristics and advantages of the embodiments of thepresent disclosure more apparent and understandable, specificimplementations of the present disclosure are illustrated in detailbelow.

BRIEF DESCRIPTION OF DRAWINGS

By reading the detailed description of embodiments below, various otheradvantages and benefits become clear to the person skilled in the art.The drawings are only used for an objective of showing the embodiments,and are not considered as a limitation to the present disclosure. Inaddition, throughout the drawings, the same reference signs are used torepresent the same or similar components.

FIG. 1 shows a schematic structural diagram of an application scenarioaccording to an embodiment of the present disclosure;

FIG. 2 shows a schematic flowchart of a method for adjusting parametersof a LiDAR according to an embodiment of the present disclosure;

FIG. 3 shows a schematic flowchart of a neural network training processaccording to an embodiment of the present disclosure;

FIG. 4 a shows a schematic diagram of an optical path for reducing avertical angle of field of view of an emitting laser beam of a LiDARaccording to an embodiment of the present disclosure;

FIG. 4 b shows a schematic diagram of an optical path for reducing adirection of an optical axis of an emitting laser beam of a LiDARaccording to an embodiment of the present disclosure;

FIGS. 5 a and 5 b show the schematic diagrams of a topological structureof a drivable area according to an embodiment of the present disclosure;

FIG. 6 shows a schematic diagram of a detection range of a LiDAR beforeand after parameter adjustment according to an embodiment of the presentdisclosure;

FIG. 7 a shows a schematic structural diagram of an application scenarioaccording to another embodiment of the present disclosure;

FIG. 7 b shows a schematic diagram of an application scenario includingthree LiDARs according to another embodiment of the present disclosure;

FIG. 7 c shows a schematic diagram of an application scenario includingfive LiDARs according to another embodiment of the present disclosure;

FIG. 8 shows a schematic flowchart of a method for adjusting parametersof a LiDAR according to another embodiment of the present disclosure;

FIG. 9 shows a schematic structural diagram of a device for adjustingparameters of a LiDAR according to an embodiment of the presentdisclosure; and

FIG. 10 shows a schematic structural diagram of a LiDAR according to anembodiment of the present disclosure.

DETAILED DESCRIPTION

The following describes embodiments of the present disclosure in a moredetailed manner with reference to accompanying drawings. Although theaccompanying drawings show the embodiments of the present disclosure, itshould be understood that the present disclosure can be implemented invarious forms and should not be limited to the embodiments describedherein. On the contrary, these embodiments are provided for morethoroughly understanding of the present disclosure and educating aperson of ordinary skill in the art with the scope of the presentdisclosure.

FIG. 1 shows a schematic diagram of an application scenario according toan embodiment of the present disclosure. As shown in FIG. 1 , theapplication scenario includes a LiDAR 101 and a vehicle 102. The LiDAR101 is mounted on various vehicles 102 that need to detect a surroundingenvironment. The vehicle 102 can be, for example, a car, a ship, anaircraft, or the like.

When the vehicle 102 is the car, the LiDAR can be mounted on the front,rear, roof, side of the car, or any other body position that can fix theLiDAR. It is understood that the LiDAR can also be fixed on structuralplatforms of other devices externally connected to the car. It can beunderstood that the structure platform can maintain synchronousoperation with the car when the car runs.

FIG. 2 shows a schematic flowchart of a method for adjusting parametersof LiDAR according to an embodiment of the present disclosure. Thismethod is applied to the LiDAR 101 in FIG. 1 . As shown in FIG. 2 , themethod includes the following steps.

Step 110: acquiring 3D environment information around the LiDAR.

The LiDAR can be a rotary mechanical LiDAR or a solid-state LiDAR. Itcan be understood that when the LiDAR is the rotary mechanical LiDAR,the 3D environmental information covers a 360-degree range of field ofview around the LiDAR. When the LiDAR is the solid-state LiDAR, the 3Denvironmental information covers a certain angular range in front of theLiDAR, for example, 120 degrees.

The 3D environment information can be 3D point cloud data. It can beunderstood that when the emitting laser beam of the LiDAR irradiates asurface of the object and is reflected back by the object to be receivedby a receiver, received laser signals are recorded in a form of points,so as to form 3D point cloud data. The 3D point cloud data can includegeometric position information and reflection intensity information. Thereflection intensity information is an intensity of echo collected bythe receiving device of the LiDAR, and the intensity information isrelated to a surface material, a roughness, a direction of an incidentangle of an object, emission energy of an instrument, and a laserwavelength.

It can be understood that, in some embodiments, the 3D environmentinformation can also be combined data of the 3D point cloud data andimage data. A color image is acquired via an image sensor, and thencolor information (RGB) of a pixel at a corresponding position isassigned with a corresponding point in a point cloud, so that the 3Dpoint cloud data has color information, wherein the image sensor can beintegrated inside the LiDAR, so that the color information correspondingto the point cloud data can be acquired before the point cloud data isoutput; thus, a semantic feature of a 3D environment can be identified.Optionally, the image sensor can also be independently arranged outsidea LiDAR and integrates the point cloud data and the image data into the3D environmental information. The image data can be image data or videodata.

It can be understood that the 3D environment information around theLiDAR is acquired in real time.

Step 120: based on the 3D environment information, identifying ascenario type where the LiDAR is positioned and a drivable area.

The scenario type where the LiDAR is positioned is related to a fieldused by the LiDAR. When the LiDAR is used in different fields, ascenario in which the LiDAR is positioned is also different, so thescenario types can also include a plurality of types. When the LiDAR isapplied to the car, the scenario type includes, but is not limited to, ahighway scenario type, an intersection scenario type, a parking lotscenario type, etc.

Based on the 3D environment information, the step of identifying thescenario type where the LiDAR is positioned and the drivable area canspecifically include:

Step 121: processing the 3D point cloud data to generate a multi-channelpoint cloud feature map.

The step of processing the 3D point cloud data to generate themulti-channel point cloud feature map specifically includes:

-   -   projecting the acquired 3D point cloud data onto an XY plane,        and then rasterizing an area within a certain range according to        the preset grid size. A grid saves different feature values to        form the multi-channel feature map. Each channel in the        multi-channel feature map represents a feature value of a        certain dimension.

The characteristic values corresponding to a plurality of channels caninclude, but are not limited to, an occupancy rate, the number ofpoints, a height of the lowest point, a height of the highest point, anaverage height, an average intensity, a distance, an azimuth, and so on.

In a specific implementation, a grid size can be determined to be 0.2meters. The ranges of the front and rear, as well as left and right areeach 80 meters, forming an 800×800 feature map. A plurality ofcombinations of the forgoing features are selected as a point cloudmulti-channel feature map. For example, four features of the occupancyrate, the number of points, the average height and the average intensityare selected to form a point cloud feature map of 800×800×4 size.

Step 122: extracting high-dimensional feature information from themulti-channel point cloud feature map.

The steps of extracting the high-dimensional feature information fromthe multichannel point cloud feature map specifically includes:inputting the multi-channel point cloud feature map into a first neuralnetwork. The first neural network acquires the high-dimensional featureinformation via a convolution operation. The high-dimensional featureinformation can include edge feature information, shape featureinformation, texture feature information, and even semantic feature,such as contour information of the car, contour information of a tollstation, contour information of a traffic light, information of a roadtraffic sign, roadside information, lane line information, informationof a pedestrian crossing, etc. It can be understood that when the 3Denvironment information is the combined data of the 3D point cloud dataand the image data, the high-dimensional feature information extractedfrom the multi-channel point cloud feature map has a color feature andcan be used for identification of the semantic feature, such as the roadsign and the traffic light with a text or a color.

Step 123: determining the scenario type according to thehigh-dimensional feature information.

The step of determining the scenario type according to thehigh-dimensional feature information specifically includes: inputtingthe high-dimensional feature information into a second neural network toacquire an output value of the scenario type, matching the output valueof the scenario type with the scenario type label according to acorresponding relationship between the output value of the scenario typeand a scenario type label, and determining the scenario typecorresponding to the output value of the scenario type to determine thescenario type where the LiDAR is positioned. It can be understood thatthe scenario type label includes an output value range of the scenariotype corresponding to the scenario type label. It can be understoodthat, the step of matching the output value of the scenario type withthe scenario type label according to the corresponding relationshipbetween the output value of the scenario type and the scenario typelabel specifically includes: matching the output value range of thescenario type with the output value of the scenario type correspondingto the scenario type label; and when the output value of the scenariotype falls within the output value range of the scenario typecorresponding to the scenario type label, determining that the scenariotype is the scenario type corresponding to the scenario type label.

For example, if the high-dimensional features such as a curved roadside,pedestrian crossing zebra stripes, the contour information of thetraffic light, etc. are input into the second neural network, thescenario output value obtained is 9. If the output value range of thescenario type corresponding to the intersection scenario label is 7-10,the label corresponding to the scenario is an intersection scenario. Forexample, if the high-dimensional feature is the pedestrian crossingzebra stripes and the contour information of the traffic light, and theoutput value of the scenario type acquired by inputting the secondneural network is 8, the scenario also falls within the scenario outputvalue range corresponding to an intersection scenario label, and thelabel corresponding to the scenario is an intersection scenario.

It can be understood that the scenario type where the LiDAR ispositioned is determined by the corresponding relationship between theoutput value of the scenario type and the scenario type label, whichincreases determination accuracy of the scenario type.

In another feasible embodiment, the steps of inputting thehigh-dimensional feature information into the second neural network toacquire the output value of the scenario type can specifically include:inputting the high-dimensional feature information into the secondneural network, and matching the high-dimensional feature informationwith the feature information in a model library of the second neuralnetwork, where it can be understood that the feature information in themodel library of the second neural network has a feature value and aweight value; and according to the matched feature value and the weightvalue, acquiring the output value of the scenario type.

Step 124: determining the drivable area according to thehigh-dimensional feature information.

The step of determining the drivable area according to thehigh-dimensional feature information specifically includes: inputtingthe high-dimensional feature information into a third neural network toacquire an output map of the drivable area, matching the output map ofthe drivable area with a drivable area label according to acorresponding relationship between the output map of the preset drivablearea and the drivable area label, and determining the drivable areacorresponding to the output map of the drivable area to determine thedrivable area.

It can be understood that the step of inputting the high-dimensionalfeature information into the third neural network to acquire the outputmap of the drivable area can include: inputting the high-dimensionalfeature information into the third neural network, and extracting, bythe third neural network, a feature of the drivable area, where thefeature of the drivable area can include, for example, the roadsideinformation, the lane line information, etc.; extracting an areacontaining the feature of the drivable area to generate the output mapof the drivable area.

It can be understood that the drivable area labels in differentscenarios can be different. For example, a drivable area label of ahighway can be a label containing the lane line information; a drivablearea label of an intersection can include the roadside information andthe lane line information; a drivable area label of a parking lot caninclude parking line information, and so on.

The step of matching the output map of the drivable area with thedrivable area label to determine the drivable area can include: matchingthe output map of the drivable area with the drivable area label,determining the scenario where the output map of the drivable area ispositioned, and determining the drivable area according to the scenario.

After the step of matching the output map of the drivable area with thedrivable area label to determine the drivable area, the method furtherincludes: acquiring information of an obstacle in the drivable area,determining the complexity of the drivable area according to theinformation of the obstacle; and if the complexity of the drivable areaexceeds a preset threshold, reacquiring the drivable area.

It can be understood that the first neural network and the second neuralnetwork each consist of a convolutional layer and a pooling layer, andthe third neural network consists of a de-convolutional layer. Thehigh-dimensional feature information is an intermediate variable, whichis acquired by the first neural network via a convolution operation, andis input into the second neural network and the third neural network,respectively.

Since a basic operation of the neural network is the convolutionoperation, which consists of a series of multiplication and additionoperations, in order to introduce a nonlinear transformation, anonlinear activation function is added to the neural network. Commonlyused nonlinear activation functions include ReLU, Sigmoid, etc.

Optionally, in some embodiments, before step 120, the method furtherincludes: acquiring trained point cloud data, where the trained pointcloud data include original point cloud data, and the scenario typelabel and the drivable area label that correspond to the original pointcloud data; and according to the original point cloud data, and thescenario type label and the drivable area label that correspond to theoriginal point cloud data, training the first neural network, the secondneural network, and the third neural network.

The original point cloud data can be acquired by a vehicle-mountedLiDAR, and the scenario type label and the drivable area label thatcorrespond to the original point cloud data can be acquired by manuallabeling. The larger the amount of the original point cloud data in atrained set formed by the original point cloud data, the more accuratethe neural network acquired by training.

As shown in FIG. 3 , the step of training the first neural network, thesecond neural network, and the third neural network can specificallyinclude: acquiring initialization parameters of the first neuralnetwork, the second neural network, and the third neural network,respectively; generating a trained point cloud feature map from theoriginal point cloud data in the trained point cloud data; inputting thetraining point cloud feature map into the first neural network, thesecond neural network, and the third neural network; acquiring thetrained scenario type output by the second neural network and a trainedarea of interest output by the third neural network; comparing thetrained scenario type with the scenario type label; calculating a lossvalue of the scenario type via a second loss function; comparing thetrained area of interest with the drivable area label; calculating aloss value of the drivable area via a third loss function; updatingparameters of the first neural network, the second neural network, andthe third neural network via a back propagation algorithm based on theloss value of the scenario type and the loss value of the drivable area;and when the loss value of the scenario type and the loss value of thedrivable area no longer changes, stopping training and saving a finalparameter value of the neural network.

Step 130: determining a parameter adjusting strategy of the LiDAR basedon the scenario type and the drivable area; and adjusting currentoperating parameters of the LiDAR based on the parameter adjustingstrategy.

In this embodiment, the current operating parameters of the LiDARinclude, but are not limited to, one or more of a vertical angle offield of view of an emitting laser beam of the LiDAR, a horizontal angleof field of view of the emitting laser beam of the LiDAR, a direction ofan optical axis of the emitting laser beam of the LiDAR, a scanningdensity of the LiDAR, a scanning frequency of the LiDAR, and a pulsetransmitting power of the LiDAR.

Step 130 can specifically include:

Step 131: according to the scenario type, determining scenario parameteradjusting strategies of one or more of the horizontal angle of field ofview of the LiDAR, the vertical angle of field of view of the LiDAR, thedirection of the optical axis of the emitting laser beam of the LiDAR,the scanning density of the LiDAR, the scanning frequency of the LiDAR,and the pulse emitting power of the LiDAR.

The scenario parameter adjusting strategy and the correspondingrelationship between the scenario type and the scenario parameteradjusting strategy are preset by the user according to the scenariotype.

For example, when the car loaded with a LiDAR drives in the highwayscenario, the LiDAR is required to focus the scanning range in the frontand rear road areas, reducing detection of the left and right sides ofthe car, and ignoring or reducing detection outside the drivable area.Reducing a detection range not only saves computing power, but alsoimproves a rate of obstacle detection in a key area. Therefore, in someembodiments, when the scenario type is detected as the highway scenariotype, the vertical angle of field of view of the LiDAR is reduced, thepulse emitting frequency of the emitting laser beam of the LiDAR isreduced, and the scanning frequency of the LiDAR is increased. Reducingthe vertical angle of field of view of the emitting laser beam by, forexample, as shown in FIG. 4 a , compressing the vertical angle of fieldof view by 20% or adjusting the vertical angle of field of view of theLiDAR from +15° ˜−25° to +12° ˜−20° can concentrate more beams in ahorizontal direction and increase the front and back detection distanceof the LiDAR, so as to detect a long-distance object. Reducing the pulseemitting frequency by, for example, adjusting the pulse emittingfrequency from 50,000 shots per second to 10,000 shots per second canincrease charging time of a pulse laser transmitter and increase laserenergy, so as to detect an object at a farther distance. Increasing thescanning frequency by, for example, adjusting the scanning frequencyfrom 10 Hz to 15 Hz can detect changes of a moving object more quickly,so as to improve safety of automatic driving.

For example, when the car loaded with the LiDAR is positioned in theintersection scenario and passes through an intersection, an object inthe surrounding environment moves quickly, and it is necessary to detectchanges of the moving object faster at this time. When the scenario typeis detected as the intersection scenario type, it is necessary to reducethe front and rear detection distances and increase the detection ofclose distances around the car so as to detect changes of the movingobject more quickly. Therefore, in some embodiments, when theintersection scenario is detected, the pulse emitting frequency of theLiDAR is increased, the direction of the optical axis of the emittinglaser beam of the LiDAR is reduced, and the scanning frequency of theLiDAR is increased. Increasing the pulse emitting frequency of the LiDARby, for example, adjusting the pulse emitting frequency from 10,000shots per second to 50,000 shots per second can acquire the changes ofthe surrounding moving objects more quickly. If the direction of theoptical axis of the emitting laser beam is reduced by, for example, asshown in FIG. 4 b , moving down the direction of the optical axis of theemitting laser beam by 20° (in a specific application, deflecting thedirection of the optical axis of the emitting laser beam from 0° (thatis, horizontal emission) to downward 20°), a detection range of theemitting laser beam is closer to the ground, which can reduce thedetection range of the LiDAR, thereby focusing on detecting ashort-range object. Increasing the scanning frequency of the LiDAR by,for example, adjusting the scanning frequency from 10 Hz to 15 Hz candetect the changes of the moving object more quickly.

For example, when the car loaded with the LiDAR enters a staticscenario, such as a parking lot, or waits for parking, the powerconsumption of the LiDAR can be reduced when the surroundings areunchanged. Therefore, in some embodiments, when the parking lot scenariotype is detected, the pulse emitting frequency of the LiDAR is reduced,the direction of the optical axis of the emitting laser beam of theLiDAR is reduced, and the scanning frequency of the LiDAR is reduced.Reducing the scanning frequency of the LiDAR by, for example, adjustingthe scanning frequency from 15 Hz to 10 Hz can reduce the powerconsumption of the LiDAR.

Step 132: according to the drivable area, adjusting the scenarioparameter adjusting strategy, determining the adjusted scenarioparameter adjusting strategy as the parameter adjusting strategy of theLiDAR, and adjusting the current operating parameters of the LiDAR basedon the parameter adjusting strategy.

Step 132 can specifically include:

Step 1321: according to the scenario type, acquiring a standard drivingarea in the scenario type and driving parameter adjusting strategies ofthe horizontal angle of field of view of the LiDAR and the verticalangle of field of view of the LiDAR that correspond to the standarddriving area.

If the corresponding relationship between the scenario type and thestandard driving area is preset, the standard driving area under thecurrent scenario type can be searched in the corresponding relationshipbetween the scenario type and the standard driving area according to thecurrent scenario type. For example, if a standard driving areacorresponding to the highway scenario type is preset as a specificstraight road area, the scenario type is determined as the highwayscenario type, and the corresponding standard driving area is searchedas the specific straight road area. At the same time, the correspondingrelationship among the scenario type, the standard driving area, and thedriving parameter adjusting strategy is provided. Ideally, the drivablearea of a certain scenario type is the standard driving area. Presetstandard parameters of an angle of field of view should be set accordingto the standard driving area. However, the actually determined drivablearea cannot exactly coincide with the standard driving area. Therefore,by presetting the corresponding relationship between the standarddriving area and the parameter adjusting strategies of the horizontalangle of field of view of the LiDAR and the vertical angle of field ofview of the LiDAR, the driving parameter adjusting strategies of thehorizontal angle of field of view of the LiDAR and the vertical angle offield of view of the LiDAR corresponding to the acquired standarddriving area can be searched in the corresponding relationship betweenthe standard driving area and the driving parameter adjusting strategiesof the horizontal angle of field of view of the LiDAR and the verticalangle of field of view of the LiDAR, according to the acquired standarddriving area.

Step 1322: matching the drivable area with the standard driving area,and readjusting the scenario parameter adjusting strategy according tomatching results and the driving parameter adjusting strategy.

In some embodiments, the steps of matching the standard driving areawith the drivable area, and adjusting the scenario parameter adjustingstrategy according to the matching results and the driving parameteradjusting strategy specifically include: analyzing a topologicalstructure of the drivable area and a topological structure of thestandard driving area; calculating a topological correlation between thetopological structure of the drivable area and the topological structureof the standard driving area; determining a scenario differencecoefficient of the drivable area relative to the standard driving areaaccording to the topological correlation, where the scenario differencecoefficient includes a translation difference coefficient, a rotationdifference coefficient, a scaling difference coefficient, etc.; andadjusting the scenario parameter adjusting strategy according to thescenario difference coefficient and the driving parameter adjustingstrategy. In some embodiments, the step of adjusting the scenarioparameter adjusting strategy according to the scenario differencecoefficient and the driving parameter adjusting strategy specificallyincludes: acquiring a standard parameter of an angle of field of view(including a standard parameter of the vertical angle of field of viewand a standard parameter of the horizontal angle of field of view) inthe driving parameter adjusting strategy; according to the scenariodifference coefficient and based on the standard parameter of the angleof field of view, calculating in an equal proportion a parameter of anew angle of field of view (including a new vertical angle of field ofview and a new horizontal angle of field of view); and according to theparameter of the new angle of field of view, adjusting the verticalangle of field of view and the horizontal angle of field of view in thescenario parameter adjusting strategy. For example, as shown in FIG. 5 a, it is assumed that the center of the topological structure of thedrivable area and the topological structure of the standard driving areaare coincident, sides thereof are parallel, a width thereof is the same,and a ratio of lengths is 5:3. Then it is determined that thetopological structure of the drivable area and the topological structureof the standard driving area has a translation difference coefficient of0, a rotation difference coefficient of 0, and a scaling differencecoefficient of (5/3)*1. For another example, as shown in FIG. 5 b , itis assumed that the center of the topological structure of the drivablearea and the topological structure of the standard driving area arecoincident, the sides thereof are parallel, a ratio of the widthsthereof is 6:4, and a ratio of the lengths is 5:4. Then it is determinedthat the topological structure of the drivable area and the topologicalstructure of the standard driving area has the translation differencecoefficient of 0, the rotation difference coefficient of 0, and thescaling difference coefficient of (6/4)*(6/4). Optionally, in some otherembodiments, when the centers of the topological structure of thedrivable area and the topological structure of the standard driving areaare not coincident or the sides thereof are not parallel, thetopological structure of the standard driving area can be keptunchanged. The topological structure of the driving area is rotated andtranslated until the centers of the topological structure of thedrivable area and the topological structure of the standard driving areaare coincident and the sides thereof are parallel. The translationdifference coefficient and the rotation difference coefficient arerecorded. Then the topological structure of the drivable area and aproportional relationship between the length and width of thetopological structure of the standard driving area are calculated, andthe scaling difference coefficient is determined.

When the translation difference coefficient, the rotation differencecoefficient, and the scaling difference coefficient are calculated, theparameter of the new angle of field of view can be calculated accordingto the translation difference coefficient, the rotation differencecoefficient, and the scaling difference coefficient. For example, whenthe translation difference coefficient, the rotation differencecoefficient, and the scaling difference coefficient are calculated, amagnification of the angle of field of view can be calculated accordingto the translation difference coefficient, the rotation differencecoefficient, the scaling difference coefficient, and a proportionalrelationship between the sides of similar graphics. In this way, theparameter of the new angle of field of view is calculated, so that whenthe parameter of the new angle of field of view is adjusted, a detectionrange of the LiDAR can completely cover the standard driving area. Foranother example, when the translation difference coefficient, rotationdifference coefficient, and scaling difference coefficient arecalculated, the direction of the optical axis of the emitting laser beamof the LiDAR can be adjusted according to the rotation differencecoefficient, so that the direction of the optical axis of the adjustedlaser beam emitted by the LIDAR is parallel to the central axis of thetopological structure of the standard driving area. The magnification ofthe angle of field of view can be calculated according to thetranslation difference coefficient, the rotation difference coefficient,the scaling difference coefficient, and the proportional relationshipbetween the sides of similar graphics. In this way, the parameter of thenew angle of field of view is calculated, so that when the parameter ofthe new angle of field of view is adjusted, the detection range of theLiDAR can completely cover the standard driving area.

It can be understood that after the state of the LiDAR is adjusted, thedifference coefficient is calculated again according to the forgoingmethod based on the new detection range and the drivable area, and theparameter of the angle of field of view (the parameters of the verticalangle of field of view and the horizontal angle of field of view) isfurther adjusted on this basis.

Step 1323: adjusting the current operating parameters of the LiDAR basedon the adjusted scenario parameter adjusting strategy.

It should be noted that the parameter adjusting range or the presetadjusted parameter value cannot exceed an executable range of the LiDAR.In some embodiments, when the parameter adjusting range or the presetadjusted range exceeds the executable range of the LiDAR, it is promptedto re-adjust a monitoring area.

For example, the step of determining the scenario parameter adjustingstrategy that matches the highway scenario type includes: compressingthe vertical angle of field of view of the emitting laser beam of theLiDAR from +45° to 40°, adjusting the pulse emitting frequency of theemitting laser beam of the LiDAR from 50,000 shots per second to 10,000shots per second, and adjusting the scanning frequency of the LiDAR from10 Hz to 15 Hz. The driving parameter adjusting strategy that matchesthe highway scenario type includes: the horizontal angle of field ofview is 120°, and the vertical angle of field of view is 35°. It isassumed that the topological correlation between the topologicalstructure of the drivable area and the topological structure of thestandard driving area is that the centers thereof are coincident, thesides thereof are parallel, the widths thereof are the same, and theratio of the lengths thereof is 3:5, that is, the translation differencecoefficient is 0, the rotation difference coefficient is 0, and thescaling difference coefficient is (3/5)*1. Then the new horizontal angleof field of view is (3/5)*120°=72°, and the new vertical angle of fieldof view is 350 (as shown in FIG. 6 ). Finally, the adjusted scenarioparameter adjusting strategy includes: adjusting the horizontal angle offield of view of the emitting laser beam of the LiDAR to 72°, adjustingthe vertical angle of field of view of the emitting laser beam of theLiDAR to 35°, adjusting the pulse emitting frequency of the emittinglaser beam of the LiDAR from 50,000 shots per second to 10,000 shots persecond, and adjusting the scanning frequency of the LiDAR from 10 Hz to15 Hz.

For example, the step of determining the scenario parameter adjustingstrategy that matches the intersection scenario type includes: adjustingthe pulse emitting frequency from 10,000 shots per second to 50,000shots per second, moving downward the direction of the optical axis ofthe emitting laser beam by 20°, and adjusting the scanning frequencyfrom 10 Hz to 15 Hz. The driving parameter adjusting strategy thatmatches the intersection scenario type includes: the horizontal angle offield of view is 120°, and the vertical angle of field of view is 90°.It is assumed the topological correlation of the topological structureof the drivable area and the topological structure of the standarddriving area is that the centers thereof are coincident, the sidesthereof are parallel, the ratio of the widths thereof is 6:4, and theratio of the lengths thereof is 6:4, that is, the translation differencecoefficient is 0, the rotation difference coefficient is 0, the scalingdifference coefficient is (6/4)*(6/4), and the new horizontal angle offield of view is calculated as (6/4)*120°=180°. Then the new verticalangle of field of view is (6/4)*90°=135°. Finally, the adjusted scenarioparameter adjusting strategy includes: adjusting the horizontal angle offield of view of the emitting laser beam of the LiDAR to 180°, adjustingthe vertical angle of field of view of the emitting laser beam of theLiDAR to 135°, adjusting the pulse emitting frequency from 10,000 shotsper second to 50,000 shots per second, moving downward the direction ofthe optical axis of the emitting laser beam by 20°, and adjusting thescanning frequency from 10 Hz to 15 Hz.

For example, the step of determining the scenario parameter adjustingstrategy that matches the parking lot scenario type includes: adjustingthe pulse emitting frequency of the emitting laser beam of the LiDARfrom 50,000 shots per second to 10,000 shots per second, moving downwardthe direction of the optical axis of the emitting laser beam of theLiDAR by 20°, and reducing the scanning frequency from 15 Hz to 10 Hz.The driving parameter adjusting strategy that matches the parking lotscenario type includes: the horizontal angle of field of view is 120°,and the vertical angle of field of view is 60°. It is assumed that thetopological correlation between the topological structure of the drivingarea and the topological structure of the standard driving area is thatthe centers thereof are coincident, the sides thereof are parallel, theratio of the widths thereof is 1:2, and the ratio of the lengths thereofis 1:2, that is, the translation difference coefficient is 0, therotation difference coefficient is 0, and the scaling differencecoefficient is (½)*(½), then the new horizontal angle of field of viewis calculated as (½)*120°=60°, and the new vertical angle of field ofview is (½)*60°=30°. Finally, the adjusted scenario parameter adjustingstrategy includes: adjusting the horizontal angle of field of view ofthe emitting laser beam of the LiDAR to 60°, adjusting the verticalangle of field of view of the emitting laser beam of the LiDAR to 30°,adjusting the pulse emitting frequency of the emitting laser beam of theLiDAR from 50,000 shots per second to 10,000 shots per second, reducingthe direction of the optical axis of the emitting laser beam of theLiDAR by 20°, and reducing the scanning frequency from 15 Hz to 10 Hz.

The embodiment of the present disclosure acquires the 3D environmentinformation around the LiDAR to identify the scenario type where theLiDAR is positioned and the drivable area based on the 3D environmentinformation, determine the parameter adjusting strategy of the LiDARaccording to the scenario type and the drivable area, and adjust thecurrent operating parameters of the LiDAR based on the parameteradjusting strategy. The embodiment of the present disclosure canautomatically adjust the operating parameters of the LiDAR according todifferent scenarios, so that the working state of the LiDAR isautomatically adjusted to a working state suitable for the currentscenario, thereby improving working efficiency of the LiDAR. Further,the embodiment of the present disclosure can also identify the currentscenario and detect the drivable area in real time, so as to adjust thescanning range of the LiDAR to be adapted to the drivable area, whichnot only can improve a detection rate of an obstacle, but also canimprove a utilization rate of computing resources and reduce powerconsumption.

FIG. 7 a shows a schematic structural diagram of an application scenarioaccording to another embodiment of the present disclosure. As shown inFIG. 7 a , the application scenario includes a LiDAR system 103 and avehicle 102. The LiDAR system 103 is mounted on various vehicles 102that need to detect the surrounding environment. The vehicle 102 can bea vehicle, a ship, an aircraft, or the like.

The LiDAR system 103 includes a plurality of LiDARs. The plurality ofLiDARs can be arranged on the same plane or on different planes. Whenthe vehicle 102 is a car, the LiDAR can be mounted at one or more placesin the front, the rear, and the roof of the vehicle. For example, asshown in FIG. 7 b , the LiDAR system 103 includes three LiDARs. ThreeLiDARs 1031, 1032 and 1033 are all arranged on the roof of the car. TheLiDAR 1031 is positioned in the center of the roof of the car. TheLiDARs 1032 and 1033 are positioned on opposite sides of the roof of thecar. The LiDAR 1031 can be configured to acquire environmentalinformation around the car and far away from the car. The LiDARs 1032and 1033 are configured to acquire environmental information on the leftand right sides of the car and close to the car, respectively. Foranother example, as shown in FIG. 7 c , the LiDAR system 103 includesfive LiDARs. The LiDAR 1034 is arranged on the roof of the car andpositioned in the center of the roof of the car. The LiDARs 1035 and1036 are arranged on the left and right sides of the car and arepositioned around a rearview mirror. The LiDAR 1037 is arranged at thefront of the car. The LiDAR 1038 is arranged at the rear of the car.

FIG. 8 shows a schematic flowchart of a method for adjusting parametersof a LiDAR according to another embodiment of the present disclosure.This method is applied to the LiDAR system 103 in FIGS. 7 a-7 c . Asshown in FIG. 8 , the method includes the following steps.

Step 210: acquiring 3D environment information around the LiDAR system.

The specific implementation of step 210 is substantially the same asthat of step 110 in the foregoing embodiments. In this embodiment, oneLiDAR in the LiDAR system is used to acquire the 3D environmentinformation around the LiDAR system, or the plurality of LiDARs in theLiDAR system can be used to acquire the 3D environment informationaround the LiDAR system. For example, as shown in FIG. 7 b , using oneLiDAR in the LiDAR system to acquire the 3D environment informationaround the LiDAR system can be specifically as follows: the LiDAR 1031in the LiDAR system 103 is used to acquire the 3D environmentinformation around the LiDAR system 103. Using the plurality of LiDARsin the LiDAR system to acquire the 3D environment information around theLiDAR system can also be specifically as follows: acquiring partial 3Denvironment information via the LiDARs 1031, 1032, and 1033 in the LiDARsystem 103 (for example, the LiDAR 1031 acquires the front 3Denvironment information, the LiDAR 1032 acquires the right 3Denvironment information, and the LiDAR 1033 acquires the left 3Denvironment information), and integrating the acquired 3D environmentinformation to acquire complete 3D environment information.

Step 220: based on the 3D environment information, identifying ascenario type where the LiDAR system is positioned and a drivable area.

The specific implementation of step 220 is substantially the same asthat of step 120 in the foregoing embodiments, which is not repeatedhere.

Step 230: determining the parameter adjusting strategy of each LiDAR inthe LiDAR system according to the scenario type and the drivable area,and adjusting the current operating parameters of each LiDAR based onthe parameter adjusting strategy of each LiDAR.

Specifically, step 230 includes:

Step 231: according to the scenario type, determining scenario parameteradjusting strategies of one or more of a horizontal angle of field ofview, a vertical angle of field of view, a direction of an optical axisof an emitting laser beam, a scanning density, a scanning frequency, anda pulse emitting power of each LiDAR of the LiDAR system.

The scenario parameter adjusting strategy and the correspondingrelationship between the scenario type and the scenario parameteradjusting strategy are preset by a user according to the scenario type.The scenario parameter adjusting strategies of different LiDARs in theLiDAR system can be different. For example, the correspondingrelationship between the scenario type and the scenario parameteradjusting strategies of different LiDARs can also be preset by the useraccording to the scenario type and the positions of different LiDARs. Inthis embodiment, the step of, according to the scenario type,determining scenario parameter adjusting strategies of one or more ofthe horizontal angle of field of view, the vertical angle of field ofview, the direction of the optical axis of the emitting laser beam, thescanning density, the scanning frequency, and the pulse emitting powerof each LiDAR of the LiDAR system can be specifically: acquiring anidentifier of the LiDAR in the LiDAR system, and determining thescenario parameter adjusting strategies corresponding to the identifiedscenario type and the acquired identifier of the LiDAR according to thecorresponding relationship among the preset scenario type, theidentifier of the LiDAR and the scenario parameter adjusting strategy.

For example, as shown in FIG. 7 b , it is assumed that the identifiersof the LiDAR 1031, 1032, and 1033 are a, b, and c, respectively. Thenthe scenario parameter adjusting strategy corresponding to theidentified scenario type and the identifiers a, b, and c is determined.When the current scenario type is detected as a highway scenario type, astep of determining the scenario parameter adjusting strategycorresponding to a is as follows: reducing the vertical angle of fieldof view of the LiDAR, reducing the pulse emitting frequency of theemitting laser beam of the LiDAR, and increasing the scanning frequencyof the LiDAR. A step of determining the scenario parameter adjustingstrategies corresponding to b and c is as follows: reducing thehorizontal angle of field of view of the LiDAR, reducing the pulseemitting frequency of the emitting laser beam of the LiDAR, increasingthe scanning frequency of the LiDAR, hence increasing front and reardetection distances of the LiDAR on a roof of a car, compressing leftand right detection distance of the LiDAR on both sides of the roof ofthe car, and detecting changes of a moving object faster. When thecurrent scenario type is detected as an intersection scenario type, thestep of determining the scenario parameter adjusting strategycorresponding to a is as follows: increasing the vertical angle of fieldof view of the LiDAR, increasing the pulse emitting frequency of theLiDAR, reducing the direction of the optical axis of the emitting laserbeam of the LiDAR, and increasing the scanning frequency of the LiDAR.The step of determining the scenario parameter adjusting strategiescorresponding to b and c is as follows: reducing the horizontal angle offield of view of the LiDAR, increasing the pulse emitting frequency ofthe LiDAR, increasing the scanning frequency of the LiDAR, making thedetection range of the emitting laser beam of the LiDAR on the roof ofthe car closer to the ground by decreasing the front and rear detectiondistances of the LiDAR on the roof of the car, compressing the left andright detection distances of the LiDAR on both sides of the roof of thecar, and making all LiDARs detect the changes of the moving objectfaster. When the current scenario type is detected as a parking lotscenario type, the step of determining the scenario parameter adjustingstrategy corresponding to a is as follows: reducing the vertical angleof field of view of the LiDAR, reducing the pulse emitting frequency ofthe LiDAR, and reducing the scanning frequency of the LiDAR. The step ofdetermining the scenario parameter adjusting strategies corresponding tob and c is as follows: reducing the horizontal angle of field of view ofthe LiDAR, reducing the pulse emitting frequency of the LiDAR, reducingthe scanning frequency of the LiDAR, hence reducing the detection rangeof the LiDAR and reducing the power consumption of the LiDAR.

In some embodiments, the parameter adjusting strategy of each LiDAR canbe determined according to a scanning area range of each LiDAR.

Before matching the standard driving area with the driving area, andadjusting the scenario parameter adjusting strategy of each LiDAR in theLiDAR system according to matching results and the driving parameteradjusting strategy of each LiDAR in the LiDAR system, the method furtherincludes: acquiring the scanning area range of each LiDAR in the LiDARsystem; and determining the parameter adjusting strategy of each LiDARaccording to the scanning area range of each LiDAR.

For the LiDAR system shown in FIG. 7 b , the center LiDAR 1031 isconfigured to detect a mid-to-far field of view. The LiDAR 1033 and theLiDAR 1032 are configured to detect a short field of view and left andright fields of view, respectively.

It can be understood that a central axis of the LiDAR 1031 isperpendicular to a horizontal plane. The central axis of the LiDAR 1031is a bottom surface normal passing through a center of a bottom surfaceof the LiDAR 1031. The LiDAR 1031 is positioned on the left side of thecenter LiDAR, and an angle between a central axis of the LiDAR 1033 andthe central axis of the LiDAR 1031 is less than zero degree. The LiDAR1032 is positioned on the right side of the LiDAR, and an angle betweena central axis of the LiDAR 1032 and the central axis of the LiDAR 1031is greater than zero degree. The central axis of the LiDAR 1032 and thecentral axis of the LiDAR 1033 are the bottom surface normals passingthrough the center of the bottom surface of the LiDAR.

It can be understood that the various LiDARs in the LiDAR system can beon the same plane or on different planes.

It can be understood that when detection requirements of the mid-to-farfield of view need to be enhanced according to the identified scenario,one or more of the scanning frequency, the scanning density, and thepulse emitting power of the LiDAR 1031 are adjusted. When detectionrequirements of the left and right fields of view of a near-field of acar need to be enhanced according to the identified scenario, one ormore of the direction of the optical axis, the scanning density, thescanning frequency and the pulse emitting power of the left LiDAR 1033and the right LiDAR 1032 can be adjusted.

In some other embodiments, the LiDAR system can also select part of theLiDARs to work or all LiDARs to work according to the identifiedscenario. For example, as shown in FIG. 7 b , when a scenario isidentified as a highway scenario (that is, it is more necessary todetect the mid-to-far field of view), the LiDAR 1031 can be selected towork, and the adjusting parameters of the LiDAR 1031 can be determinedaccording to the scenario and the driving area. When the scenario isidentified as an intersection scenario (that is, detection of an objectaround a car body needs to be enhanced), all the LiDARs in the systemcan be switched on to work at the same time, and the operatingparameters of the LiDAR 1033 and the LiDAR 1032 in the system (as shownin FIG. 7 b ) are adjusted.

It can be understood that as shown in FIG. 7 c , the LiDAR system 103can also include five LiDARs. The center LiDAR 1034 is configured todetect the mid-to-far field of view. The LiDAR 1035 and the LiDAR 1036are configured to detect the short field of view on the left and right,respectively. The LiDARs 1037 and 1038 are configured to detect frontand back short fields of view.

When the LiDAR system includes five LiDARs, the LiDAR system determinesthe parameter adjusting strategy of each LiDAR according to the scanningarea range of each LiDAR. Specifically, for example, as shown in FIG. 7c , when the scenario is identified as the highway scenario (that is, itis more necessary to detect the mid-to-far field of view), the LiDAR1034 can be selected to work, and the adjusting parameters of the LiDAR1034 can be determined according to the scenario and the drivable area.When the scenario is identified as an intersection scenario (that is,detection of an object around a car body needs to be enhanced), all theLiDARs in the system can be switched on to work at the same time, andthe operating parameters of the LiDAR 1035, the LiDAR 1036, the LiDAR1037, and the LiDAR 1038 in the system (as shown in FIG. 7 c ) areadjusted.

Step 232: according to the drivable area, adjusting the scenarioparameter adjusting strategy of each LiDAR, determining the adjustedscenario parameter adjusting strategy of each LiDAR as the parameteradjusting strategy of each LiDAR, and adjusting the current operatingparameters of each LiDAR based on the parameter adjusting strategy ofeach LiDAR, respectively.

Step 232 can specifically include:

Step 2321: according to the scenario type, acquiring a standard drivingarea in the scenario type and driving parameter adjusting strategies ofthe horizontal angle of field of view and the vertical angle of field ofview of the LiDARs of the LiDAR system that correspond to the standarddriving area.

The corresponding relationship between the scenario type and thestandard driving area is preset, and the standard driving area under thecurrent scenario type can be searched in the corresponding relationshipbetween the scenario type and the standard driving area according to thecurrent scenario type. For example, if a standard driving areacorresponding to the highway scenario type is preset as a specificstraight road area, then when the scenario type is determined as thehighway scenario type, the corresponding standard driving area issearched to be the specific straight road area. At the same time, thecorresponding relationship among the scenario type, the standard drivingarea, and the driving parameter adjusting strategy is provided. Ideally,the drivable area of a certain scenario type is the standard drivingarea. Preset standard parameters of an angle of field of view can be setaccording to the standard driving area. However, the actually determineddrivable area cannot exactly coincide with the standard driving area.Therefore, by presetting the corresponding relationship between thestandard driving area and the parameter adjusting strategies of thehorizontal angle of field of view and the vertical angle of field ofview of each LiDAR of the LiDAR system, the driving parameter adjustingstrategies of the horizontal angle of field of view and the verticalangle of field of view of each LiDAR corresponding to the acquiredstandard driving area can be searched in the corresponding relationshipbetween the standard driving area and the driving parameter adjustingstrategies of the horizontal angle of field of view and the verticalangle of field of view of each LiDAR of the LiDAR system, according tothe acquired standard driving area.

Step 2322: matching the standard driving area with the drivable area,and adjusting the scenario parameter adjusting strategy of each LiDAR inthe LiDAR system according to matching results and the driving parameteradjusting strategy of each LiDAR in the LiDAR system.

In some embodiments, the steps of matching the standard driving areawith the drivable area, and adjusting the scenario parameter adjustingstrategy of each LiDAR in the LiDAR system according to matching resultsand the driving parameter adjusting strategy of each LiDAR in the LiDARsystem specifically include: analyzing a topological structure of thedrivable area and a topological structure of the standard driving area;calculating a topological correlation between the topological structureof the drivable area and the topological structure of the standarddriving area; determining a scenario difference coefficient of thedrivable area relative to the standard driving area according to thetopological correlation, where the scenario difference coefficientincludes a translation difference coefficient, a rotation differencecoefficient, a scaling difference coefficient, etc.; and adjusting thescenario parameter adjusting strategy of each LiDAR in the LiDAR systemaccording to the scenario difference coefficient and the drivingparameter adjusting strategy of each LiDAR in the LiDAR system. In someembodiments, the steps of adjusting the scenario parameter adjustingstrategy of each LiDAR in the LiDAR system according to the scenariodifference coefficient and the driving parameter adjusting strategy ofeach LiDAR in the LiDAR system specifically includes: acquiring thestandard parameter of the angle of field of view (including a standardparameter of the vertical angle of field of view and a standardparameter of the horizontal angle of field of view) in the drivingparameter adjusting strategy of a certain LiDAR; according to thescenario difference coefficient and based on the standard parameter ofthe angle of field of view, calculating in an equal proportion aparameter of a new angle of field of view (including a new verticalangle of field of view and a new horizontal angle of field of view);according to the parameter of the new angle of field of view, adjustingthe vertical angle of field of view and the horizontal angle of field ofview in the scenario parameter adjusting strategy of the LiDAR; andadjusting the scenario parameter adjustment strategy of each LIDAR inthe LIDAR system in the same way.

Step 2323: adjusting the current operating parameters of each LiDAR inthe LiDAR system based on the scenario parameter adjusting strategy ofeach LiDAR in the LiDAR system.

For example, as shown in FIG. 7 b , it is assumed that the identifiersof the LiDARs 1031, 1032, and 1033 are a, b, and c, respectively. Whenthe current scenario type is detected as the highway scenario type, thescenario parameter adjusting strategy corresponding to a is determinedas follows: compressing the vertical angle of field of view of theemitting laser beam from +90° to 80°, adjusting the pulse emittingfrequency of the emitting laser beam from 50,000 shots per second to10,000 shots per second, and adjusting the scanning frequency from 10 Hzto 15 Hz. The step of determining the corresponding scenario parameteradjusting strategies of b and c is as follows: compressing thehorizontal angle of field of view of the emitting laser beam from +1800to 120°, adjusting the pulse emitting frequency of the emitting laserbeam from 50,000 shots per second to 10,000 shots per second, adjustingthe scanning frequency from 10 Hz to 15 Hz. The step of determining thedriving parameter adjusting strategy corresponding to a is as follows:the horizontal angle of field of view is 120°, and the vertical angle offield of view is 60°. The step of determining the driving parameteradjusting strategies corresponding to b and c is as follows: thehorizontal angle of field of view is 100°, and the vertical angle offield of view is 35°. It is assumed that the topological correlationbetween the topological structure of the drivable area and thetopological structure of the standard driving area is that the centersthereof are coincident, the sides thereof are parallel, the widthsthereof are the same, and the ratio of the lengths thereof is 3:5, thatis, the translation difference coefficient is 0, the rotation differencecoefficient is 0, and the scaling difference coefficient is (3/5)*1. Thenew horizontal angle of field of view corresponding to a is calculatedas (3/5)*120°=72°, and the new vertical angle of field of view is 60°.The new horizontal angles of field of view corresponding to b and c are(3/5)*100°=60°, and the new vertical angle of field of view is 35°.Finally, the step of acquiring the adjusted scenario parameter adjustingstrategy corresponding to a is as follows: adjusting the horizontalangle of field of view of the emitting laser beam to 72°, adjusting thevertical angle of field of view of the emitting laser beam to 60°,adjusting the pulse emitting frequency of the emitting beam from 50,000shots per second to 10,000 shots per second, and adjusting the scanningfrequency from 10 Hz to 15 Hz. The adjusted scenario parameter adjustingstrategies corresponding to b and c are as follows: adjusting thehorizontal angle of field of view of the emitting beam to 60°, adjustingthe vertical angle of field of view of the emitting laser beam to 35°,adjusting the pulse emitting frequency of the emitting beam from 50,000shots per second to 10,000 shots per second, and adjusting the scanningfrequency from 10 Hz to 15 Hz.

The embodiment of the present disclosure acquires the 3D environmentinformation around the LiDAR system to identify the scenario type wherethe LiDAR system is positioned and the drivable area based on the 3Denvironment information, determine the parameter adjusting strategy ofeach LiDAR in the LiDAR system according to the scenario type and thedrivable area, and adjust the current operating parameters of each LiDARbased on the parameter adjusting strategy of each LiDAR. The embodimentof the present disclosure can automatically adjust the operatingparameters of each LiDAR of the LiDAR system according to differentscenarios, so that the working state of the LiDAR system isautomatically adjusted to a working state suitable for the currentscenario, thereby improving working efficiency. Further, the embodimentof the present disclosure can also identify the current scenario anddetect the drivable area in real time, so as to adjust the scanningrange of each LiDAR of the LiDAR system to be adapted to the drivablearea, which not only can improve a detection rate of an obstacle, butalso can improve a utilization rate of computing resources and reducepower consumption.

FIG. 9 shows a schematic structural diagram of a device for adjustingparameters of a LiDAR according to an embodiment of the presentdisclosure. The device can be applied to a LiDAR or a LiDAR system inthe forgoing embodiments. As shown in FIG. 10 , the device 600 includesan environmental information acquisition module 610, an identificationmodule 620, and an adjusting module 630.

The environmental information acquisition module 610 is configured toacquire 3D environmental information around the LiDAR.

The identification module 620 is configured to identify the scenariotype and the drivable area of the LiDAR based on the 3D environmentinformation. The adjusting module 630 is configured to determine theparameter adjusting strategy of the LiDAR according to the scenario typeand the drivable area, and adjust the current operating parameters ofthe LiDAR based on the parameter adjusting strategy.

In some embodiments, the 3D environment information includes 3D pointcloud data. The identification module 620 is specifically configured to:process the 3D point cloud data to generate a multi-channel point cloudfeature map; extract high-dimensional feature information from themulti-channel point cloud feature map; determine the scenario typeaccording to the high-dimensional feature information; and determine thedrivable area according to the high-dimensional feature information.

In some embodiments, the identification module 620 is specificallyconfigured to: input the multi-channel point cloud feature map into afirst neural network, and acquire the high-dimensional featureinformation output by the first neural network; input thehigh-dimensional feature information into a second neural network, andacquire an output value of a scenario type output by the second neuralnetwork; determine the scenario type corresponding to the output valueof the scenario type according to the corresponding relationship betweenan output value of the preset scenario type and the scenario type label;input the high-dimensional feature information into a third neuralnetwork, and acquire an output map of the drivable area output by thethird neural network; and determine the drivable area corresponding tothe output map of the drivable area according to the correspondingrelationship between an output map of a preset drivable area and adrivable area label.

In some embodiments, the adjusting module 630 is specifically configuredto: according to the scenario type, determine scenario parameteradjusting strategies of one or more of a horizontal angle of field ofview of the LiDAR, a vertical angle of field of view of the LiDAR, adirection of an optical axis of an emitting laser beam of the LiDAR, ascanning density of the LiDAR, a scanning frequency of the LiDAR, and apulse emitting power of the LiDAR; adjust the scenario parameteradjusting strategy according to the drivable area; determine theadjusted scenario parameter adjusting strategy as the parameteradjusting strategy of the LiDAR, and adjust the current operatingparameters of the LiDAR based on the parameter adjusting strategy.

In some embodiments, the adjusting module 630 is specifically furtherconfigured to: according to the scenario type, acquire a standarddriving area in the scenario type and driving parameter adjustingstrategies of the horizontal angle of field of view of the LiDAR and thevertical angle of field of view of the LiDAR that correspond to thestandard driving area, match the standard driving area with the drivablearea, adjust the scenario parameter adjusting strategy according to thematching results and the driving parameter adjusting strategy, andadjust the current operating parameters of the LiDAR based on theadjusted scenario parameter adjusting strategy.

In some embodiments, the adjusting module 630 is specifically furtherconfigured to: analyze a topological structure of the drivable area anda topological structure of the standard driving area; calculate atopological correlation between the topological structure of thedrivable area and the topological structure of the standard drivingarea; determine a scenario difference coefficient of the drivable arearelative to the standard driving area according to the topologicalcorrelation; and adjust the scenario parameter adjusting strategyaccording to the scenario difference coefficient and the drivingparameter adjusting strategy.

In some embodiments, the current operating parameters of the LiDARcomprise one or more of the vertical angle of field of view of theemitting laser beam of the LiDAR, the horizontal angle of field of viewof the emitting laser beam of the LiDAR, the direction of the opticalaxis of the emitting laser beam of the LiDAR, the scanning frequency ofthe LiDAR, and the pulse emitting frequency of the LiDAR.

It should be noted that the device for adjusting parameters of the LiDARaccording to the embodiment of the present disclosure is a device thatcan execute the forgoing method for adjusting the parameters of theLiDAR. All embodiments of the forgoing method for adjusting theparameters of the LiDAR are applicable to the device, and can achievethe same or similar beneficial effects.

This embodiment acquires the 3D environment information around the LiDARto identify the scenario type where the LiDAR is positioned and thedrivable area based on the 3D environment information, determine theparameter adjusting strategy of the LiDAR according to the scenario typeand the drivable area, and adjust the current operating parameters ofthe LiDAR based on the parameter adjusting strategy. The embodiment ofthe present disclosure can automatically adjust the operating parametersof the LiDAR according to different scenarios, so that the working stateof the LiDAR is automatically adjusted to a working state suitable forthe current scenario, thereby improving working efficiency of the LiDAR.Further, the embodiment of the present disclosure can also identify thecurrent scenario and detect the drivable area in real time, so as toadjust the scanning range of the LiDAR to be adapted to the drivablearea, which not only can improve a detection rate of an obstacle, butalso can improve a utilization rate of computing resources and reducepower consumption.

An embodiment of the present disclosure also provides a computer storagemedium. At least one executable instruction is stored in the computerstorage medium, and causes the processor to execute the steps of theforgoing method for adjusting the parameters of the LiDAR.

The embodiment of the present disclosure also provides a computerprogram product, including a computer program stored on a computerstorage medium. The computer program includes program instructions. Whenthe program instructions are executed by a computer, the computerprogram executes the forgoing method for adjusting the parameters of theLiDAR in any of the foregoing method embodiments.

FIG. 10 shows a schematic structural diagram of a LiDAR according to anembodiment of the present disclosure, and specific embodiments of thepresent disclosure do not limit the specific implementation of theLiDAR.

As shown in FIG. 10 , the LiDAR can include: an emitting device 701, areceiving device 702, a processor 703, a communication interface 704, amemory 706, and a communication bus 708.

The emitting device 701 is configured to emit emergent laser to adetection area, and the receiving device 702 is configured to receiveecho laser reflected by an object in the detection area. The emittingdevice 701 is specifically configured to scan and emit the emergentlaser to the detection area to scan an object in the detection area.

The processor 703, the communication interface 704, and the memory 706communicate with each other via the communication bus 708. Thecommunication interface 704 is configured to communicate with otherdevices, such as a client, other servers, or other network elements. Theprocessor 703 is connected to the emitting device 701 via thecommunication interface 704, and configured to execute the program 710,which can specifically execute the forgoing method for adjusting theparameters of the LiDAR in any of the foregoing method embodiments.

Specifically, the program 710 can include a program code, and theprogram code includes computer operation instructions.

The processor 703 can be a central processing unit (CPU), or anApplication Specific Integrated Circuit (ASIC), or one or moreintegrated circuits configured to implement the embodiments of thepresent disclosure. One or more processors included in the computingdevice can be the same type of processor, such as one or more CPUs, ordifferent types of processors, such as one or more CPUs and one or moreASICs.

The memory 706 is configured to store program 710. The memory 706 caninclude a high-speed RAM memory, and can also include a non-volatilememory, for example, at least one magnetic disk memory.

This embodiment acquires the 3D environment information around the LiDARto identify the scenario type where the LiDAR is positioned and thedrivable area based on the 3D environment information, determine theparameter adjusting strategy of the LiDAR according to the scenario typeand the drivable area, and adjust the current operating parameters ofthe LiDAR based on the parameter adjusting strategy. The embodiment ofthe present disclosure can automatically adjust the operating parametersof the LiDAR according to different scenarios, so that the working stateof the LiDAR is automatically adjusted to a working state suitable forthe current scenario, thereby improving working efficiency of the LiDAR.Further, the embodiment of the present disclosure can also identify thecurrent scenario and detect the drivable area in real time, so as toadjust the scanning range of the LiDAR to be adapted to the drivablearea, which not only can improve a detection rate of an obstacle, butalso can improve a utilization rate of computing resources and reducepower consumption.

The algorithms or displays provided here are not inherently related toany particular computer, virtual system, or other equipment. Variousgeneral-purpose systems can also be used in conjunction with theteachings based on this. Based on the forgoing description, thestructure required to construct this type of system is obvious. Inaddition, the embodiments of the present disclosure are not directed toany specific programming language. It should be understood that variousprogramming languages can be used to implement the content of thepresent disclosure described herein.

In the specification provided here, a lot of specific details aredescribed. However, it can be understood that embodiments of the presentdisclosure can be practiced without these specific details. In someinstances, common methods, structures, and technologies are not shown indetail, so as not to obscure the understanding of this specification.

Similarly, it should be understood that in order to streamline thepresent disclosure and help understand one or more of the variousinventive aspects, in the forgoing description of the exemplaryembodiments of the present disclosure, the various features of theembodiments of the present disclosure are sometimes grouped togetherinto a single implementation, example, diagram, or description. However,the disclosed method should not be interpreted as reflecting theintention that the claimed invention requires more features than thoseexplicitly stated in each claim.

The person skilled in the art can understand that it is possible toadaptively change the modules in a device in the embodiment. The modulescan be arranged in one or more devices different from the embodiment.The modules or units or assemblies in the embodiments can be combinedinto one module or unit or assembly. In addition, the modules or unitsor assemblies can be divided into a plurality of sub-modules orsub-units or sub-assemblies. Unless expressly stated otherwise, eachfeature disclosed in this specification (including the accompanyingclaims, abstract and drawings) can be replaced by an alternative featureproviding the same, equivalent, or similar objective.

In addition, the person skilled in the art can understand that althoughsome embodiments herein include certain features included in otherembodiments but not other features, the combination of features ofdifferent embodiments means that the combinations of features ofdifferent embodiments fall within the scope of the present disclosureand form different embodiments.

It should be noted that the forgoing embodiments illustrate rather thanlimit the present disclosure, and the person skilled in the art candesign alternative embodiments without departing from the scope of theappended claims. In the claims, any reference signs between parenthesesshould not be constructed as a limitation to the claims. The word“comprising” or “comprise” does not exclude the presence of elements orsteps not listed in the claims. The word “a” or “an” that precedes anelement does not exclude the presence of a plurality of such elements.The invention can be implemented by means of hardware comprising aplurality of different elements and by means of a suitably programmedcomputer. In the unit claims enumerating a plurality of devices, aplurality of these devices can be embodied in the same hardware item.The use of the words “first,” “second,” “third,” etc. do not indicateany order. These words can be interpreted as names. Unless otherwisespecified, the steps in the foregoing embodiments should not beunderstood as a limitation on an execution order.

What is claimed is:
 1. A method for adjusting parameters of a LiDAR,comprising: acquiring 3D environment information around the LiDAR;identifying a scenario type where the LiDAR is positioned and a drivablearea based on the 3D environment information; determining a parameteradjusting strategy of the LiDAR based on the scenario type and thedrivable area; and adjusting current operating parameters of the LiDARbased on the parameter adjusting strategy.
 2. The method according toclaim 1, wherein the 3D environment information comprises 3D point clouddata, and wherein identifying the scenario type where the LiDAR ispositioned and the drivable area based on the 3D environment informationcomprises: processing the 3D point cloud data to generate amulti-channel point cloud feature map; extracting high-dimensionalfeature information from the multi-channel point cloud feature map;determining the scenario type according to the high-dimensional featureinformation; and determining the drivable area according to thehigh-dimensional feature information.
 3. The method according to claim2, wherein extracting the high-dimensional feature information from themulti-channel point cloud feature map comprises: inputting themulti-channel point cloud feature map into a first neural network, andacquiring the high-dimensional feature information output by the firstneural network; determining the scenario type according to thehigh-dimensional feature information comprises: inputting thehigh-dimensional feature information into a second neural network,acquiring an output value of the scenario type output by the secondneural network, and determining the scenario type corresponding to theoutput value of the scenario type according to a correspondingrelationship between the output value of the scenario type and ascenario type label; and determining the drivable area according to thehigh-dimensional feature information comprises: inputting thehigh-dimensional feature information into a third neural network,acquiring an output map of the drivable area output by the third neuralnetwork, and determining the drivable area corresponding to the outputmap of the drivable area according to a corresponding relationshipbetween an output map of a preset drivable area and a drivable arealabel.
 4. The method according to claim 1, wherein determining theparameter adjusting strategy of the LiDAR based on the scenario type andthe drivable area, and adjusting current operating parameters of theLiDAR based on the parameter adjusting strategy comprise: according tothe scenario type, determining scenario parameter adjusting strategiesof one or more of a horizontal angle of field of view of the LiDAR, avertical angle of field of view of the LiDAR, a direction of an opticalaxis of an emitting laser beam of the LiDAR, a scanning density of theLiDAR, a scanning frequency of the LiDAR, and a pulse emitting power ofthe LiDAR; and according to the drivable area, adjusting the scenarioparameter adjusting strategy, determining the adjusted scenarioparameter adjusting strategy as the parameter adjusting strategy of theLiDAR, and adjusting the current operating parameters of the LiDAR basedon the parameter adjusting strategy.
 5. The method according to claim 4,wherein adjusting the scenario parameter adjusting strategy according tothe drivable area, determining the adjusted scenario parameter adjustingstrategy as the parameter adjusting strategy of the LiDAR, and adjustingthe current operating parameters of the LiDAR based on the parameteradjusting strategy comprise: according to the scenario type, acquiring astandard driving area in the scenario type and driving parameteradjusting strategies of the horizontal angle of field of view of theLiDAR and the vertical angle of field of view of the LiDAR thatcorrespond to the standard driving area; matching the drivable area withthe standard driving area, and readjusting the scenario parameteradjusting strategy according to matching results and the drivingparameter adjusting strategy; and adjusting the current operatingparameters of the LiDAR based on the adjusted scenario parameteradjusting strategy.
 6. The method according to claim 5, wherein matchingthe drivable area with the standard driving area, and readjusting thescenario parameter adjusting strategy according to the matching resultsand the driving parameter adjusting strategy comprise: analyzing atopological structure of the drivable area and a topological structureof the standard driving area; calculating a topological correlationbetween the topological structure of the drivable area and thetopological structure of the standard driving area; determining ascenario difference coefficient of the drivable area relative to thestandard driving area according to the topological correlation; andadjusting the scenario parameter adjusting strategy according to thescenario difference coefficient and the driving parameter adjustingstrategy.
 7. The method according to claim 1, wherein the currentoperating parameters of the LiDAR comprise one or more of a verticalangle of field of view of an emitting laser beam of the LiDAR, ahorizontal angle of field of view of the emitting laser beam of theLiDAR, a direction of an optical axis of the emitting laser beam of theLiDAR, a scanning frequency of the LiDAR, and a pulse emitting frequencyof the LiDAR.
 8. A LiDAR, comprising: an emitting device, a receivingdevice, a processor, a memory, a communication interface, and acommunication bus, wherein the processor, the memory, and thecommunication interface complete mutual communication with each othervia the communication bus; the emitting device is configured to emitemergent laser to a detection area; the receiving device is configuredto receive echo laser reflected by an object in the detection area; andthe memory is configured to store at least one executable instruction,and the executable instruction causes the processor to execute the stepsof the method for adjusting the parameters of the LiDAR according toclaim 1, and to adjust operating parameters of the emitting device.
 9. Amethod for adjusting parameters of LiDAR, applied to a LiDAR systemcomprising a plurality of LiDARs, the method comprising: acquiring 3Denvironmental information around the LiDAR system; identifying ascenario type where the LiDAR system is positioned and a drivable areabased on the 3D environment information; according to the scenario type,determining scenario parameter adjusting strategies of one or more of ahorizontal angle of field of view of at least one LiDAR, a verticalangle of field of view of the at least one LiDAR, a direction of anoptical axis of an emitting laser beam of the at least one LiDAR, ascanning density of the at least one LiDAR, a scanning frequency of theat least one LiDAR, and a pulse emitting power of the at least one LiDARof the LiDAR system; and according to the drivable area, adjusting thescenario parameter adjusting strategy of the at least one LiDAR,determining the adjusted scenario parameter adjusting strategy as theparameter adjusting strategy of the at least one LiDAR, and adjustingthe current operating parameter of the at least one LiDAR based on theparameter adjusting strategy.
 10. The method according to claim 9,wherein acquiring the 3D environmental information around the LiDARsystem comprises: acquiring 3D environmental information around theLiDAR system via one LiDAR in the LiDAR system, wherein the one LiDAR isa LiDAR that detects a mid-to-far detection field of view; or acquiringthe 3D environmental information around the LiDAR system via theplurality of LiDARs in the LiDAR system, wherein the plurality of LiDARscorrespond to different detection fields of view, and the 3D environmentinformation acquired by the plurality of LiDARs are integrated toacquire a complete 3D environment information of the LiDAR system. 11.The method according to claim 9, wherein identifying the scenario typewhere the LiDAR system is positioned and the drivable area based on the3D environment information, the method further comprising: according tothe scenario type, determining a scanning area range corresponding tothe scenario type; determining a scanning area range of each LiDAR inthe LiDAR system; and determining whether a LiDAR in the LiDAR system isin a working state according to the scanning area range corresponding tothe scenario type and the scanning area range of each LiDAR in the LiDARsystem.
 12. The method according to claim 11, wherein determiningwhether the LiDAR in the LiDAR system is in the working state accordingto the scanning area range corresponding to the scenario type and thescanning area range of each LiDAR in the LiDAR system comprises:selecting the LiDAR for detecting the mid-to-far detection field of viewin the LiDAR system to work when the identified scenario type is ahighway scenario; and selecting all LiDARs in the LiDAR system to workwhen the identified scenario type is an intersection scenario.
 13. Themethod according to claim 9, wherein, according to the scenario type,determining the scenario parameter adjusting strategies of one or moreof the horizontal angle of field of view of the at least one LiDAR, thevertical angle of field of view of the at least one LiDAR, the directionof the optical axis of the emitting laser beam of the at least oneLiDAR, the scanning density of the at least one LiDAR, the scanningfrequency of the at least one LiDAR, and the pulse emitting power of theat least one LiDAR of the LiDAR system comprises: acquiring anidentifier of each LiDAR in the LiDAR system; and according to acorresponding relationship among the preset scenario type, theidentifier of the at least one LiDAR in the LiDAR system, and thescenario parameter adjusting strategy, determining the scenarioparameter adjusting strategies of one or more of the horizontal angle offield of view of the at least one LiDAR, the vertical angle of field ofview of the at least one LiDAR, the direction of the optical axis of theemitting laser beam of the at least one LiDAR, the scanning density ofthe at least one LiDAR, and the pulse emitting power of the at least oneLiDAR of the LiDAR system corresponding to the scenario type and theidentifier of the at least one LiDAR in the LiDAR system.
 14. The methodaccording to claim 9, wherein adjusting the scenario parameter adjustingstrategy of the at least one LiDAR according to the drivable area,determining the adjusted scenario parameter adjusting strategy as theparameter adjusting strategy of the at least one LiDAR, and adjustingthe current operating parameter of at least one LiDAR based on theparameter adjusting strategy comprise: according to the scenario type,acquiring a standard driving area in the scenario type and drivingparameter adjusting strategies of the horizontal angle of field of viewand the vertical angle of field of view of the at least one LiDAR of theLiDAR system corresponding to the standard driving area; matching thestandard driving area with the drivable area, and adjusting the scenarioparameter adjusting strategy of the at least one LiDAR in the LiDARsystem according to matching results and the driving parameter adjustingstrategy of the at least one LiDAR in the LiDAR system; and adjustingthe current working parameter of the at least one LiDAR in the LiDARsystem based on the scenario parameter adjusting strategy of the atleast one LiDAR in the LiDAR system.
 15. The method according to claim14, wherein matching the standard driving area with the drivable area,and adjusting the scenario parameter adjusting strategy of the at leastone LiDAR in the LiDAR system according to matching results and thedriving parameter adjusting strategy of the at least one LiDAR in theLiDAR system comprise: analyzing a topological structure of the drivablearea and a topological structure of the standard driving area;calculating a topological correlation between the topological structureof the drivable area and the topological structure of the standarddriving area; determining a scenario difference coefficient of thedrivable area relative to the standard driving area according to thetopological correlation; and adjusting the scenario parameter adjustingstrategy of the at least one LiDAR in the LiDAR system according to thescenario difference coefficient and the driving parameter adjustingstrategy of the at least one LiDAR in the LiDAR system.
 16. The methodaccording to claim 15, wherein adjusting the scenario parameteradjusting strategy of the at least one LiDAR in the LiDAR systemaccording to the scenario difference coefficient and the drivingparameter adjusting strategy of the at least one LiDAR in the LiDARsystem comprise: acquiring a standard parameter of field of view in thedriving parameter adjusting strategy of the at least one LiDAR in theLiDAR system; according to the scenario difference coefficient,calculating a new parameter of field of view in an equal proportionbased on the standard parameter of field of view; and according to thenew parameter of field of view, adjusting the vertical angle of field ofview and the horizontal angle of field of view in the scenario parameteradjusting strategy of the at least one LiDAR in the LiDAR system.